montelss is a package to perform cosmological inference from large-scale structures. For the moment, it contains:

A flexible framework to perform minimization, profiling (based on iminuit) and MCMC (based on emcee) of likelihoods. montelss also integrates a least-square solver. Likelihoods for RSD and BAO are in likelihoods/RSD and likelihoods/BAO and use the pyspectrum package (see below). analyze_fits.py and analyze_mcmc.py contain a lot of routines for post-processing.

A code to compute theory power spectrum:

• RSD model (same as in https://arxiv.org/abs/1904.08851v3), using pyregpt
• BAO templates (isotropic and anisotropic)

Apply survey geometry effects:

• window functions, following Wilson et al. 2015: https://arxiv.org/abs/1511.07799, using pycute
• integral constraint corrections, following de Mattia et al. 2019: https://arxiv.org/abs/1904.08851v3, using pycute
• wide-angle contributions, following Beutler et al. 2019: https://arxiv.org/abs/1810.05051
• fiber collisions, following Hahn et al. 2016: https://arxiv.org/abs/1609.01714

montelss and pyspectrum will eventually be two separate, independent packages.

• scipy
• fftlog
• iminuit
• emcee
• pathos
• class
• pyregpt
• pycute